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C3N: content-constrained convolutional network for mural image completion

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Abstract

Ancient murals, suffering from severe diseases, usually exhibit the absence or distortion of local areas. The damaged murals severely impaired people’s visual appreciation and satisfaction in the digital conservation of cultural heritage. However, there is no large amount of murals due to their scarcity. In this paper, we propose a novel content-constrained convolutional network for mural image completion. This method employs frequency transformation to facilitate effective multi-scale feature fusion for image inpainting, taking into account both space and frequency domains. Our network uses adaptive space-varying activation functions to correct feature maps across scales. Our network also uses dual-domain partial convolution with a mask for computing on only valid points, whereas the mask is updated for the next layer. This iterative process is performed until the mask is filled to build the repaired image. The proposed method is verified on the datasets in comparison with baseline methods. The experimental results demonstrate that the proposed method achieves better results with less artifacts in repairing mural images and generally outperforms the state-of-the-art methods both quantitatively and qualitatively. The code and pretrained models are available at https://github.com/zhangyongqin/C3N.

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Data availibility

The datasets used during the current study are from the public Places dataset (http://places2.csail.mit.edu/index.html) or available from the authors on reasonable request.

Notes

  1. https://github.com/SeitaroShinagawa/chainer-partial_convolution_image_inpainting.

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Acknowledgements

This work was supported by the Social Science Foundation of Shaanxi Province (Grant No. 2019H010), the National Social Science Foundation of China (Grant No. 20BKG031), the New Star of Youth Science and Technology of Shaanxi Province (Grant No. 2020KJXX-007), the Natural Science Basic Research Program of Shaanxi (Program No. 2019JM-103), the Open Research Fund of CAS Key Laboratory of Spectral Imaging Technology (Grant No. LSIT201920W), the National Natural Science Foundation of China (Grant No. 62173270), the Xi’an Key Laboratory of Intelligent Perception and Cultural Inheritance (Grant No. 2019219614SYS011CG033), the Key Research and Development Program of Shaanxi (Program No. 2021ZDLGY15-06), and the Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT\(\_\)17R87).

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Author notes

  1. Xianlin Peng and Huayu Zhao have contributed equally to this work.

Authors and Affiliations

  1. Artificial Intelligence Research Institute, Northwest University, Xi’an, 710127, Shaanxi, China

    Xianlin Peng, Huayu Zhao, Xiaoyu Wang, Yongqin Zhang, Zhan Li, Jun Wang & Jinye Peng

  2. Art School, Northwest University, Xi’an, 710127, Shaanxi, China

    Xianlin Peng

  3. School of Information Science and Technology, Northwest University, Xi’an, 710127, Shaanxi, China

    Huayu Zhao, Xiaoyu Wang, Yongqin Zhang, Zhan Li, Jun Wang & Jinye Peng

  4. Cultural Relics Protection Office, Shaanxi History Museum, Xi’an, 710061, Shaanxi, China

    Qunxi Zhang

  5. School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, England, UK

    Haida Liang

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  1. Xianlin Peng
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Peng, X., Zhao, H., Wang, X. et al. C3N: content-constrained convolutional network for mural image completion. Neural Comput & Applic 35, 1959–1970 (2023). https://doi.org/10.1007/s00521-022-07806-0

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